{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93723569-e97b-4e07-9b7b-344f06f9dfa7",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ourownstory/neural_prophet/blob/master/tutorials/feature-use/crossvalidation.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2af163-c378-4e01-8d5b-3def6194c29e",
   "metadata": {},
   "source": [
    "# Test and CrossValidate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80400b6d-ca57-47ba-9dc5-0da3885ab6b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'google.colab' in str(get_ipython()):\n",
    "    !pip install git+https://github.com/ourownstory/neural_prophet.git # may take a while\n",
    "    #!pip install neuralprophet # much faster, but may not have the latest upgrades/bugfixes\n",
    "    \n",
    "import pandas as pd\n",
    "from neuralprophet import NeuralProphet, set_log_level\n",
    "set_log_level(\"ERROR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e79793-8ebb-4d06-b021-82b49d107653",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90e2ee7e-6798-4a0c-a37f-c3b7325d9c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_location = \"https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/\"\n",
    "# df = pd.read_csv(data_location + \"air_passengers.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24aaf32f-f3c4-4ac0-9317-122f4df932e7",
   "metadata": {},
   "source": [
    "## 1. Basic: Train and Test a model\n",
    "First, we show how to fit a model and evaluate it on a holdout set.\n",
    "\n",
    "Note: before making any actual forecasts, re-fit the model on all data available, else you are greatly reducing your forecast accuracy!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e4871e-c8a5-48cb-b86c-de75adbbaf24",
   "metadata": {},
   "source": [
    "### 1.1 Train-Test evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "170af980-1830-4909-800a-b3e4f0ccc4f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                              \r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SmoothL1Loss</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007121</td>\n",
       "      <td>31.413279</td>\n",
       "      <td>1440.266357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SmoothL1Loss        MAE          MSE\n",
       "0      0.007121  31.413279  1440.266357"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = NeuralProphet(seasonality_mode= \"multiplicative\", learning_rate = 0.1)\n",
    "\n",
    "df = pd.read_csv(data_location + \"air_passengers.csv\")\n",
    "df_train, df_test = m.split_df(df=df, freq=\"MS\", valid_p=0.2)\n",
    "\n",
    "metrics_train = m.fit(df=df_train, freq=\"MS\")\n",
    "metrics_test = m.test(df=df_test)\n",
    "\n",
    "metrics_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafa6a99-0df5-43cf-a235-0af2a51a4bcf",
   "metadata": {},
   "source": [
    "### 1.2 Bonus: Predict into future\n",
    "Before making any actual forecasts, re-fit the model on all data available, else you are greatly reducing your forecast accuracy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47d23260-ad96-4196-aba1-7eb9fd2e6b21",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                              \r"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_train2 = m.fit(df=df, freq=\"MS\")\n",
    "future = m.make_future_dataframe(df, periods=24, n_historic_predictions=48)\n",
    "forecast = m.predict(future)\n",
    "fig = m.plot(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7960ba2-6d3a-464c-837a-e8f844839d59",
   "metadata": {},
   "source": [
    "### 1.3 Bonus: Visualize training\n",
    "If you installed the `[live]` version of NeuralProphet, you can additionally visualize your training progress and spot any overfitting by evaluating every epoch.\n",
    "\n",
    "Note: Again, before making any predictions, re-fit the model with the entire data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db5b66bb-9734-49f9-af09-bc497509e040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                              \r"
     ]
    }
   ],
   "source": [
    "m = NeuralProphet(seasonality_mode=\"multiplicative\", learning_rate=0.1)\n",
    "\n",
    "df = pd.read_csv(data_location + \"air_passengers.csv\")\n",
    "df_train, df_test = m.split_df(df=df, freq=\"MS\", valid_p=0.2)\n",
    "\n",
    "metrics = m.fit(df=df_train, freq=\"MS\", validation_df=df_test, plot_live_loss=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c84678f9-219f-4c7f-acca-3ff5b958855d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SmoothL1Loss</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RegLoss</th>\n",
       "      <th>SmoothL1Loss_val</th>\n",
       "      <th>MAE_val</th>\n",
       "      <th>MSE_val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>0.000317</td>\n",
       "      <td>6.178366</td>\n",
       "      <td>64.096661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005971</td>\n",
       "      <td>28.745131</td>\n",
       "      <td>1207.533936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SmoothL1Loss       MAE        MSE  RegLoss  SmoothL1Loss_val    MAE_val  \\\n",
       "307      0.000317  6.178366  64.096661      0.0          0.005971  28.745131   \n",
       "\n",
       "         MSE_val  \n",
       "307  1207.533936  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0507f5-e4c6-4d97-845f-98a32cd0c64a",
   "metadata": {},
   "source": [
    "## 2. Time-series Cross-Validation\n",
    "Time-series crossvalidation is also known as rolling origin backtest. In the first fold, we start with some data to train the model, and evaluate over the next `fold_pct` data points. In the next fold, the previous evaluation data is added to training, and evaluation starts later (forecast orgin 'rolls' forward), again measuring accuracy over the next section of data. We repeat this, until the final folds evaluation data reaches the end of the data.\n",
    "\n",
    "Note: before making any actual forecasts, re-fit the model on all data available, else you are greatly reducing your forecast accuracy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbf84c93-dc7c-406d-941a-87ae5b8adafa",
   "metadata": {},
   "outputs": [],
   "source": [
    "METRICS = ['SmoothL1Loss', 'MAE', 'MSE']\n",
    "params = {\"seasonality_mode\": \"multiplicative\", \"learning_rate\": 0.1}\n",
    "\n",
    "df = pd.read_csv(data_location + \"air_passengers.csv\")\n",
    "folds = NeuralProphet(**params).crossvalidation_split_df(df, freq=\"MS\", k=5, fold_pct=0.20, fold_overlap_pct=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b585c737-08f6-468e-890a-558348f6b9ee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                              \r"
     ]
    }
   ],
   "source": [
    "metrics_train = pd.DataFrame(columns=METRICS)\n",
    "metrics_test = pd.DataFrame(columns=METRICS)\n",
    "\n",
    "for df_train, df_test in folds:\n",
    "    m = NeuralProphet(**params)\n",
    "    train = m.fit(df=df_train, freq=\"MS\")\n",
    "    test = m.test(df=df_test)\n",
    "    metrics_train = metrics_train.append(train[METRICS].iloc[-1])\n",
    "    metrics_test = metrics_test.append(test[METRICS].iloc[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e09fc84b-3e11-47be-aa88-024b0c27c8a1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SmoothL1Loss</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.012356</td>\n",
       "      <td>22.827064</td>\n",
       "      <td>883.100308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.012997</td>\n",
       "      <td>8.211850</td>\n",
       "      <td>508.476321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.003793</td>\n",
       "      <td>10.655480</td>\n",
       "      <td>198.452499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.034445</td>\n",
       "      <td>29.894016</td>\n",
       "      <td>1331.016846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SmoothL1Loss        MAE          MSE\n",
       "mean      0.012356  22.827064   883.100308\n",
       "std       0.012997   8.211850   508.476321\n",
       "min       0.003793  10.655480   198.452499\n",
       "max       0.034445  29.894016  1331.016846"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_test.describe().loc[[\"mean\", \"std\", \"min\", \"max\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d151730f-3354-47da-9d92-15abc3c87e2a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2 Using Benchmark Framework\n",
    "The benchmark framework is designed for a 2 phase evaluation pipeline. This is in most cases sufficient, particularly when using crossvalidation. For the remainder of this tutorial we will be using normal Train-Test or Cross-Validation evaluation setups.\n",
    "\n",
    "Note: The Benchmarking Framework does currently not properly support auto-regression or lagged covariates with multiple step ahead forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f23fd06b-2f0c-4f2c-92fa-5ad28e286d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralprophet.benchmark import Dataset, NeuralProphetModel, SimpleExperiment, CrossValidationExperiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87473a1c-e394-4b21-bba8-ae107671f114",
   "metadata": {},
   "source": [
    "### 2.1 SimpleExperiment\n",
    "Let's set up a train test experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d050b7-2b8e-40d1-ba4d-e43e22a64c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = Dataset(df = pd.read_csv(data_location + \"air_passengers.csv\"), name = \"air_passengers\", freq = \"MS\")\n",
    "params = {\"seasonality_mode\": \"multiplicative\"}\n",
    "exp = SimpleExperiment(\n",
    "    model_class=NeuralProphetModel,\n",
    "    params=params,\n",
    "    data=ts,\n",
    "    metrics=[\"MASE\", \"RMSE\"],\n",
    "    test_percentage=25,\n",
    ")\n",
    "result_train, result_test = exp.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b05621e-844f-4291-a81b-109e424829e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': 'air_passengers',\n",
       " 'model': 'NeuralProphet',\n",
       " 'params': \"{'seasonality_mode': 'multiplicative'}\",\n",
       " 'MASE': 0.5662454805745694,\n",
       " 'RMSE': 28.829688840169517}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# result_train\n",
    "result_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3e88e1-d1b9-46cc-bd8d-88dbf2f8fd93",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.2 CrossValidationExperiment\n",
    "Let's crossvalidate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a5812bd-45d5-4a45-af9f-8dac25524e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = Dataset(df = pd.read_csv(data_location + \"air_passengers.csv\"), name = \"air_passengers\", freq = \"MS\")\n",
    "params = {\"seasonality_mode\": \"multiplicative\"}\n",
    "exp_cv = CrossValidationExperiment(\n",
    "    model_class=NeuralProphetModel,\n",
    "    params=params,\n",
    "    data=ts,\n",
    "    metrics=[\"MASE\", \"RMSE\"],\n",
    "    test_percentage=10,\n",
    "    num_folds=3,\n",
    "    fold_overlap_pct=0,\n",
    "  )\n",
    "result_train, result_test = exp_cv.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a5d79f1-f61f-4c7d-9709-15535f1be8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': 'air_passengers',\n",
       " 'model': 'NeuralProphet',\n",
       " 'params': \"{'seasonality_mode': 'multiplicative'}\",\n",
       " 'MASE': [0.5044070952378811, 0.3214836090858653, 0.6877225952888634],\n",
       " 'RMSE': [21.220949835639722, 16.892885303566555, 32.749710284868435]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd37a7c-17eb-4f6f-a7c8-f4ac15c41183",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3. Advanced: 3-Phase Train, Validate and Test procedure\n",
    "Finally, in 3.1 and 3.2, we will do a 3-part data split to do a proper training, validation and test evaluation of your model. This setup is used if you do not want to bias your performance evaluation by your manual hyperparameter tuning. this is, however not common when working with time series, unless you work in academia. Crossvalidation is usually more than adequate to evaluate your model performance.\n",
    "\n",
    "If you are confused by this, simply ignore this section and continue your forecasting life. Or if you got curious, read up on how to evaluate machine learning models to level up your skills. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44add497-fd4b-4926-9338-9b507bbec4e3",
   "metadata": {},
   "source": [
    "### 3.1 Train, Validate and Test evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e342fef-b57d-4ab8-8550-f9311cb0e032",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = NeuralProphet(seasonality_mode= \"multiplicative\", learning_rate = 0.1)\n",
    "\n",
    "df = pd.read_csv(data_location + \"air_passengers.csv\")\n",
    "# create a test holdout set:\n",
    "df_train_val, df_test = m.split_df(df=df, freq=\"MS\", valid_p=0.2)\n",
    "# create a validation holdout set:\n",
    "df_train, df_val = m.split_df(df=df_train_val, freq=\"MS\", valid_p=0.2)\n",
    "\n",
    "# fit a model on training data and evaluate on validation set.\n",
    "metrics_train1 = m.fit(df=df_train, freq=\"MS\")\n",
    "metrics_val = m.test(df=df_val)\n",
    "\n",
    "# refit model on training and validation data and evaluate on test set.\n",
    "metrics_train2 = m.fit(df=df_train_val, freq=\"MS\")\n",
    "metrics_test = m.test(df=df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "635e284c-b3b7-477c-ada0-9a131912137f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SmoothL1Loss</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>0.000368</td>\n",
       "      <td>5.348336</td>\n",
       "      <td>46.359198</td>\n",
       "      <td>train1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>0.000571</td>\n",
       "      <td>6.835680</td>\n",
       "      <td>72.002425</td>\n",
       "      <td>train2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005073</td>\n",
       "      <td>18.293804</td>\n",
       "      <td>639.260681</td>\n",
       "      <td>validate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002529</td>\n",
       "      <td>15.051163</td>\n",
       "      <td>318.605957</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SmoothL1Loss        MAE         MSE     split\n",
       "324      0.000368   5.348336   46.359198    train1\n",
       "649      0.000571   6.835680   72.002425    train2\n",
       "0        0.005073  18.293804  639.260681  validate\n",
       "0        0.002529  15.051163  318.605957      test"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_train1[\"split\"]  = \"train1\"\n",
    "metrics_train2[\"split\"]  = \"train2\"\n",
    "metrics_val[\"split\"] = \"validate\"\n",
    "metrics_test[\"split\"] = \"test\"\n",
    "metrics_train1.tail(1).append([metrics_train2.tail(1), metrics_val, metrics_test]).drop(columns=['RegLoss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56864317-2b2f-4332-8d76-40a40e287b9d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3.2 Train, Cross-Validate and Cross-Test evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dac90c4f-63e7-42f2-b49e-f6b3177e070f",
   "metadata": {},
   "outputs": [],
   "source": [
    "METRICS = ['SmoothL1Loss', 'MAE', 'MSE']\n",
    "params = {\"seasonality_mode\": \"multiplicative\", \"learning_rate\": 0.1}\n",
    "\n",
    "df = pd.read_csv(data_location + \"air_passengers.csv\")\n",
    "folds_val, folds_test = NeuralProphet(**params).double_crossvalidation_split_df(df, freq=\"MS\", k=5, valid_pct=0.10, test_pct=0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96d8154e-c77a-4836-ad92-9b205e88b268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "metrics_train1 = pd.DataFrame(columns=METRICS)\n",
    "metrics_val = pd.DataFrame(columns=METRICS)\n",
    "for df_train1, df_val in folds_val:\n",
    "    m = NeuralProphet(**params)\n",
    "    train1 = m.fit(df=df_train, freq=\"MS\")\n",
    "    val = m.test(df=df_val)\n",
    "    metrics_train1 = metrics_train1.append(train1[METRICS].iloc[-1])\n",
    "    metrics_val = metrics_val.append(val[METRICS].iloc[-1])\n",
    "\n",
    "metrics_train2 = pd.DataFrame(columns=METRICS)\n",
    "metrics_test = pd.DataFrame(columns=METRICS)\n",
    "for df_train2, df_test in folds_test:\n",
    "    m = NeuralProphet(**params)\n",
    "    train2 = m.fit(df=df_train2, freq=\"MS\")\n",
    "    test = m.test(df=df_test)\n",
    "    metrics_train2 = metrics_train2.append(train2[METRICS].iloc[-1])\n",
    "    metrics_test = metrics_test.append(test[METRICS].iloc[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c5ea1cbc-fbb2-46b1-9a43-4547f8b6113a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SmoothL1Loss</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000269</td>\n",
       "      <td>6.724876</td>\n",
       "      <td>74.517579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.090767</td>\n",
       "      <td>1.726542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SmoothL1Loss       MAE        MSE\n",
       "mean      0.000269  6.724876  74.517579\n",
       "std       0.000023  0.090767   1.726542"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_train2.describe().loc[[\"mean\", \"std\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "13f96119-df3c-4a0d-adeb-f2816ff88587",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SmoothL1Loss</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.009112</td>\n",
       "      <td>29.609479</td>\n",
       "      <td>1148.171173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.007296</td>\n",
       "      <td>13.811697</td>\n",
       "      <td>919.264304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SmoothL1Loss        MAE          MSE\n",
       "mean      0.009112  29.609479  1148.171173\n",
       "std       0.007296  13.811697   919.264304"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_val.describe().loc[[\"mean\", \"std\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c6327f8-bcb3-4454-bc9f-a1fe26f8bea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SmoothL1Loss</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.001103</td>\n",
       "      <td>14.406253</td>\n",
       "      <td>295.007996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.001310</td>\n",
       "      <td>7.844408</td>\n",
       "      <td>337.153951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SmoothL1Loss        MAE         MSE\n",
       "mean      0.001103  14.406253  295.007996\n",
       "std       0.001310   7.844408  337.153951"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_test.describe().loc[[\"mean\", \"std\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be076455-5393-4628-9079-7efa60b8e404",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
  },
  "kernelspec": {
   "display_name": "np-dev",
   "language": "python",
   "name": "np-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
